U.S. patent application number 16/396606 was filed with the patent office on 2019-12-19 for diagnostic method and system.
The applicant listed for this patent is United Arab Emirates University. Invention is credited to Mahmoud F. Y. Al Ahmad, Mohamed Al Hemairy, Saad Amin.
Application Number | 20190380661 16/396606 |
Document ID | / |
Family ID | 68838923 |
Filed Date | 2019-12-19 |
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United States Patent
Application |
20190380661 |
Kind Code |
A1 |
Al Ahmad; Mahmoud F. Y. ; et
al. |
December 19, 2019 |
Diagnostic Method And System
Abstract
Self-diagnosis of diseases is highly desired and very popular
nowadays. The present application provides system, methodology, and
the like for providing real-time detection of a medical
condition.
Inventors: |
Al Ahmad; Mahmoud F. Y.; (Al
Ain, AE) ; Al Hemairy; Mohamed; (Al Ain, AE) ;
Amin; Saad; (Coventry, GB) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
United Arab Emirates University |
Al Ain |
|
AE |
|
|
Family ID: |
68838923 |
Appl. No.: |
16/396606 |
Filed: |
April 26, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15383481 |
Dec 19, 2016 |
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16396606 |
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62377223 |
Aug 19, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 2505/07 20130101;
A61B 5/1118 20130101; A61B 5/7246 20130101; A61B 5/087 20130101;
A61B 5/01 20130101; A61B 5/747 20130101; A61B 5/0533 20130101; A61B
2562/0219 20130101; A61B 5/14542 20130101; A61B 2562/029 20130101;
A61B 5/02416 20130101; A61B 5/746 20130101; A61B 5/02208 20130101;
A61B 5/0816 20130101; A61B 5/14532 20130101; A61B 5/0488 20130101;
A61B 5/7264 20130101; A61B 5/0402 20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00 |
Claims
1. A system for passively diagnosing a subject, the system
comprising: at least one sensor couplable to the subject for
collecting at least one measurement from the subject; at least one
storage device for storing the collected at least one measurement;
and at least one processor in communication with the at least one
sensor, the at least one processor configured to: obtain the at
least one measurement; determine a weighting factor value to the at
least one sensor; determine a control value for the at least one
sensor, the control value based on the at least one measurement
from the at least one sensor; determine an indicator value based on
the at least one measurement, the weighting factor value and the
control value; access a database stored on the at least one storage
device, the database having at least one predetermined indicator
value corresponding to a pre-identified disease; and diagnosing
presence of a disease in the subject by solely relying on the at
least one measurement and by matching the determined indicator
value with the at least one pre-determined indicator value of the
pre-identified disease.
2. The system according to claim 1, wherein the at least one
measurement is a physiological measurement corresponding to a vital
sign of the subject.
3. The system according to claim 1, wherein the control value is a
binary value determined as 0 when the at least one measurement is
within a known normal range for the at least one pre-determined
disease and is determined as 1 when the at least one measurement is
outside the normal range for the at least one pre-determined
disease.
4. The system according to claim 1, wherein the weighting factor
value is the ratio of a number of pre-determined diseases for which
the at least one sensor is used to obtain a measurement over a
total number of pre-determined diseases in the database.
5. The system according to claim 1, the processor is further
configured to determine a minimum value for the at least one
pre-determined indicator based on the weighting factor value of the
at least one sensor, a pre-determined minimum range value
measurable by the at least one sensor and the control value of the
sensor and to determine a maximum value for the pre-determined
indicator based on the weighting factor value of the at least one
sensor, a pre-determined maximum range value measurable by the at
least one sensor and the control value of the sensor, wherein the
minimum value and maximum value are stored in the database.
6. The system according to claim 5, wherein the processor is
configured to diagnose the subject as normal if the at least one
measurement falls within the pre-determined minimum range value and
the predetermined maximum range value for a pre-determined
disease.
7. The system according to claim 5, wherein the processor is
configured to diagnose the subject as having the pre-determined
disease if the indicator value falls within the minimum value and
the maximum value for the least one pre-determined disease.
8. The system according to claim 7, the processor is further
configured to notify at least one of the subject, a doctor, a
hospital, an emergency contact and an emergency mobile unit of the
diagnosed disease of the subject.
9. The system according to claim 3, wherein when the at least one
sensor is assigned a control value of 0, the processor is
configured to eliminate the at least one sensor from further
consideration thereby reducing processing time.
10. A method of diagnosing a subject, the method comprising
configuring at least one processor to perform the steps of:
receiving at least one measurement from at least one sensor
non-invasively couplable to the subject; determining a weighting
factor value to the at least one sensor; determining a control
value for the at least one sensor, the control value based on the
at least one measurement from the at least one sensor; determining
an indicator value based on the at least one measurement, the
weighting factor value and the control value; accessing a database
stored on at least one storage device, the database having at least
one predetermined indicator value corresponding to a pre-identified
disease; and diagnosing presence of a disease in the subject by
solely relying on the at least one measurement and by matching the
determined indicator value with the at least one pre-determined
indicator value of the pre-identified disease.
11. The method according to claim 10, wherein determining the
control value comprising assigning a value of 0 when the at least
one measurement is within a known normal range for the at least one
pre-determined disease and is assigned a value of 1 when the at
least one measurement is outside the normal range for the at least
one pre-determined disease.
12. The method according to claim 10, wherein determining the
weighting factor comprises determining a ratio of a number of
pre-determined diseases for which the at least one sensor is used
to obtain a measurement over a total number of pre-determined
diseases in the database.
13. The method according to claim 10, the method further comprising
configuring the at least one processor to further perform the steps
of determining a minimum value for the at least one pre-determined
indicator based on the weighting factor value of the at least one
sensor, a pre-determined minimum range value measurable by the at
least one sensor and the control value of the sensor and
determining a maximum value for the pre-determined indicator based
on the weighting factor value of the at least one sensor, a
pre-determined maximum range value measurable by the at least one
sensor and the control value of the sensor, and storing the
determined minimum value and maximum value in the database.
14. The method according claim 13, wherein diagnosing presence of a
disease in the subject comprises diagnosing the subject as normal
if the at least one measurement falls within the pre-determined
minimum range value and the predetermined maximum range value for a
pre-determined disease.
15. The method according claim 13, wherein diagnosing presence of a
disease in the subject comprises diagnosing the subject as having
the pre-determined disease if the indicator value falls within the
minimum value and the maximum value for the least one
pre-determined disease.
16. The method according to claim 15, the method further comprising
notifying at least one of the subject, a doctor, a hospital, an
emergency contact and an emergency mobile unit of the diagnosed
disease of the subject.
17. The system according to claim 11, wherein by assigning the at
least one sensor a control value of 0, configuring the processor to
eliminate the at least one sensor from further consideration
thereby reducing processing time.
18. The method according to claim 10, the method further
comprising: adding a new pre-determined disease to the database;
and modifying the weighting factor value based on the added
pre-determined disease, thereby enhancing the accuracy of the
weighing factor.
19. The system according to claim 1, wherein the disease is at
least one of a physical, physiological or emotional disease.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation in part application from
U.S. application Ser. No. 15/383,481, filed on Dec. 19, 2016, which
claims priority to U.S. Provisional Application No. 62/377,223,
filed on Aug. 19, 2016, the entirety of both applications are
hereby incorporated by reference.
FIELD OF THE INVENTION
[0002] The present disclosure relates to disease detection and
related system and methodology.
BACKGROUND
[0003] Vital signs are commonly used to monitor human's body basic
functions. Examples of vital signs that are frequently monitored
are body temperature, blood pressure, heart rate, and breathing
rate. These indicators help in assessing the physical health of a
person by providing diagnosis of possible diseases and checking
treatment progress towards recovery. There is a desire in the field
for an inexpensive, efficient, accurate and consistent disease
diagnostic system and method, that does not rely on the
subjectivity of the physician nor the feedback of the patient.
SUMMARY OF THE INVENTION
[0004] The current disclosure describes multiple aspects and
embodiments. In one aspect, a system for passively diagnosing a
subject is described. An exemplary embodiment of that system
comprises: at least one sensor couplable to the subject for
collecting at least one measurement from the subject. The system
also includes at least one storage device for storing the collected
at least one measurement and at least one processor in
communication with the at least one sensor. The at least one
processor is configured to: obtain the at least one measurement;
determine a weighting factor value to the at least one sensor;
determine a control value for the at least one sensor, where the
control value is based on the at least one measurement from the at
least one sensor; determine an indicator value based on the at
least one measurement, the weighting factor value and the control
value; access a database stored on the at least one storage device,
where the database have at least one predetermined indicator value
corresponding to a pre-identified disease; and diagnosing presence
of a disease in the subject by solely relying on the at least one
measurement and by matching the determined indicator value with the
at least one pre-determined indicator value of the pre-identified
disease.
[0005] In a related embodiment, the at least one measurement is a
physiological measurement corresponding to a vital sign of the
subject. In another related embodiment, the disease is related to a
physical and/or emotional condition of the subject.
[0006] In a related embodiment, the control value is a binary value
determined as 0 when the at least one measurement is within a known
normal range for the at least one pre-determined disease and is
determined as 1 when the at least one measurement is outside the
normal range for the at least one pre-determined disease.
[0007] In a related embodiment, the weighting factor value is found
as the ratio of a number of pre-determined diseases for which the
at least one sensor is used to obtain a measurement over a total
number of pre-determined diseases in the database.
[0008] In yet another related embodiment, the processor is further
configured to determine a minimum value for the at least one
pre-determined indicator based on the weighting factor value of the
at least one sensor, a pre-determined minimum range value
measurable by the at least one sensor and the control value of the
sensor and to determine a maximum value for the pre-determined
indicator based on the weighting factor value of the at least one
sensor, a pre-determined maximum range value measurable by the at
least one sensor and the control value of the sensor, wherein the
minimum value and maximum value are stored in the database.
[0009] In a further related embodiment, the processor is configured
to diagnose the subject as normal if the at least one measurement
falls within the pre-determined minimum range value and the
predetermined maximum range value for a pre-determined disease. The
processor may also be configured to diagnose the subject as having
the pre-determined disease if the indicator value falls within the
minimum value and the maximum value for the least one
pre-determined disease.
[0010] In a related embodiment, the processor is further configured
to notify at least one of the subject, a doctor, a hospital, an
emergency contact and an emergency mobile unit of the diagnosed
disease of the subject.
[0011] In one related embodiment, when the at least one sensor is
assigned a control value of 0, the processor is configured to
eliminate the at least one sensor from further consideration
thereby reducing processing time.
[0012] Another aspect of the invention may be described as a method
of diagnosing a subject, the method comprising configuring at least
one processor to perform the steps of: receiving at least one
measurement from at least one sensor non-invasively couplable to
the subject; determining a weighting factor value to the at least
one sensor; determining a control value for the at least one
sensor, the control value based on the at least one measurement
from the at least one sensor; determining an indicator value based
on the at least one measurement, the weighting factor value and the
control value; accessing a database stored on at least one storage
device, where the database having at least one predetermined
indicator value corresponding to a pre-identified disease; and
diagnosing presence of a disease in the subject by solely relying
on the at least one measurement and by matching the determined
indicator value with the at least one pre-determined indicator
value of the pre-identified disease.
[0013] In a related embodiment, the step of determining the control
value comprises assigning a value of 0 when the at least one
measurement is within a known normal range for the at least one
pre-determined disease and is assigned a value of 1 when the at
least one measurement is outside the normal range for the at least
one pre-determined disease.
[0014] In another related embodiment, the step of determining the
weighting factor comprises determining a ratio of a number of
pre-determined diseases for which the at least one sensor is used
to obtain a measurement over a total number of pre-determined
diseases in the database.
[0015] In yet another embodiment, the method further comprises
configuring the at least one processor to further perform the steps
of determining a minimum value for the at least one pre-determined
indicator based on the weighting factor value of the at least one
sensor, a pre-determined minimum range value measurable by the at
least one sensor and the control value of the sensor and
determining a maximum value for the pre-determined indicator based
on the weighting factor value of the at least one sensor, a
pre-determined maximum range value measurable by the at least one
sensor and the control value of the sensor, and storing the
determined minimum value and maximum value in the database.
[0016] In a related embodiment, the step of diagnosing presence of
a disease in the subject comprises diagnosing the subject as normal
if the at least one measurement falls within the pre-determined
minimum range value and the predetermined maximum range value for a
pre-determined disease.
[0017] In a related embodiment, the step of diagnosing presence of
a disease in the subject comprises diagnosing the subject as having
the pre-determined disease if the indicator value falls within the
minimum value and the maximum value for the least one
pre-determined disease.
[0018] In another related embodiment, the method further comprising
notifying at least one of the subject, a doctor, a hospital, an
emergency contact and an emergency mobile unit of the diagnosed
disease of the subject.
[0019] In a further related embodiment, by assigning the at least
one sensor a control value of 0, the method include configuring the
processor to eliminate the at least one sensor from further
consideration thereby reducing processing time.
[0020] In another related embodiment, the method further comprises
adding a new pre-determined disease to the database; and modifying
the weighting factor value based on the added pre-determined
disease, thereby enhancing the accuracy of the weighing factor.
BRIEF DESCRIPTION OF THE DRAWINGS
[0021] FIG. 1: shows a workflow diagram illustrating an online
system architecture according to an embodiment of the current
invention. The workflow has four stages: a, b, c, and d.
[0022] FIG. 2: shows a platform for measuring biometrics according
to an exemplary embodiment of the invention.
[0023] FIG. 3: shows a medical condition detection system according
to an exemplary embodiment of the invention.
[0024] FIG. 4: shows an exemplary disease diagnosis system.
[0025] FIG. 5: shows a pseudocode for Disease Search Algorithm
according to an embodiment of the invention.
[0026] FIG. 6: shows an exemplary eHealth test bench system.
[0027] FIG. 7: shows a wearable sensor simulator system according
to an exemplary embodiment of the invention.
[0028] FIG. 8: shows an evaluation system according to an
embodiment of the invention, the system comprising a simulator,
gateway, display, and a server.
[0029] FIG. 9: shows a flowchart of data transfer from sensors
simulator (Peripheral) to medical gateway (Central).
[0030] FIG. 10: shows transfer time from medical gateway to server
over several tests.
[0031] FIG. 11: shows pseudocode for a Sequential Search Algorithm
that is used in the prior art.
[0032] FIG. 12: shows a comparison chart of disease detection time
using the exemplary eHealth system and lookup table.
[0033] FIG. 13: Real-time testing on server for performance in
detecting diseases.
DETAILED DESCRIPTION
[0034] Vital signs such as body temperature, blood pressure, heart
rate, and breathing rate, by way of nonlimiting examples, are
commonly used to monitor human's body basic functions, These
indicators help in assessing the physical health of a person by
providing diagnosis of possible diseases, and checking treatment
progress towards recovery. Table 1 shows some common diseases along
with their corresponding medical conditions and sensors used to
measure associated vital sign alteration. Table 1 also provides a
brief description of each disease.
TABLE-US-00001 TABLE 1 Defined diseases and corresponding medical
conditions Disease Description Vital signs ranges Associated
sensor/s Bradycardia abnormally slow heart rate <60 beats/min
HR_SENSOR Tachycardia abnormally fast heart rate >100 OR >
120 beats/min HR_SENSOR Hypotension abnormally low blood pressure
BP < 100 mm Hg systolic BP_SENSOR Hypertension abnormally high
blood pressure Mild to moderate (systolic BP_SENSOR blood pressure
< 180 mm Hg and diastolic blood pressure below 110 mm Hg) Severe
hypertension, BP_SENSOR defined as a systolic pressure > 180 mm
Hg or diastolic pressure > 110 mm Hg, Hypoxaemia abnormally low
concentration of oxygen SP02 < 95% SP02_SENSOR in the blood
Hyperthermia abnormally high body temperature core temperature >
37.80.degree. C. TEMP_SENSOR Hypothermia Abnormally low body
temperature core temperature < 36.0.degree. C. TEMP_SENSOR
Bradypnea abnormally slow breathing rate RR < 20 breaths/min
RR_SENSOR Tachypnea abnormally fast breathing rate RR > 25
breaths/min RR_SENSOR Sinus P waves are hidden within each
preceding ECG image "camel hump" ECG_SENSOR Tachycardia T wave
appearance Prediabetes blood sugar level is higher than normal
Fasting glucose level: GLOCOSE_SENSOR but not yet high enough to be
classified as (100-125) (mg/dL) type 2 diabetes Diabetes describes
a group of metabolic diseases in Fasting glucose level:
GLOCOSE_SENSOR which the person has high blood glucose more than
125 (mg/dL) (blood sugar), either because insulin production is
inadequate, or because the body's cells do not respond properly to
insulin, or both Pneumonia a disease of the lungs characterized RR
> 25 breaths/min RR_SENSOR especially by inflammation and HR
> 100 OR HR > 120 beats/min HR_SENSOR consolidation of lung
tissue followed by core temperature > 37.80.degree. C.
TEMP_SENSOR resolution and by fever, chills, cough, and difficulty
in breathing and that is caused especially by infection Urosepsis
is a systemic reaction of the body (SIRS) core temperature >
37.80.degree. C. TEMP_SENSOR to a bacterial infection of the
urogenital HR > 100 or HR > 120 beats/min HR_SENSOR organs
with the risk of life-threatening BP < 100 mm Hg systolic
BP_SENSOR symptoms including shock Asthma is a chronic inflammatory
disorder of the 90% < SPO2 < 95% SP02_SENSOR Moderate airways
100 < HR < 120 beats/min HR_SENSOR RR > 25 breaths/min
RR_SENSOR Asthma is sever chronic inflammatory disorder of SP02
< 90% SP02_SENSOR Severe the airways HR > 120 beats/min
HR_SENSOR RR > 25 breaths/min RR_SENSOR Respiratory is the
cessation of normal breathing due SP02 < 90% SP02_SENSOR Arrest
to failure of the lungs to function effectively HR < 60
beats/min HR_SENSOR Imminent RR > 30 breaths/min RR_SENSOR
[0035] Detection and identification of diseases at early stage can
facilitate and possibly improve success of the treatment
significantly. Unfortunately, due to the load of the daily work,
most people do not find enough time to visit the doctor. On the
other hand, due to the frequent increment of diseases nowadays, it
becomes impossible for the physicians to recall all symptoms and
medical conditions for all kind of diseases. Adequate assistive
tools are necessary not only to help quickly identify the diseases
but also to minimize medical mistakes and avoid prescribing
inaccurate or unnecessary medications or treatments. Online
diagnosis system may be used to provide such diagnosis services. IN
such systems, the accurate detection and identification of a
disease is highly dependable on the method used for diagnosis.
[0036] However, disease diagnosis is a very sophisticated process
and demands high and advance level of expertise and it is an
expensive and taxing process in terms of computational time and
energy consumption. A highly selective and efficient web-based
clinical expert system is not yet developed in spite of the ongoing
and existing trails and available systems. Existing expert system
incorporates inference rules. Those rules play significant role in
suggesting specific methods for disease diagnosis and treatment.
Currently, there are several reports on e-health management systems
that employ different diagnostic tools. There is are ongoing
scientific discussions and debate about which kind of diseases
should be included in medical diagnosis expert system along with
their symptoms, which factors should be considered in diagnosis for
such system and what approach should be followed, etc.
[0037] The current disclosure provides system, methodology, and the
like for diagnosing any kind of disease. In one embodiment, the
current disclosure provides a system comprising one or more
computing devices configured to perform operations consistent with
an algorithm order to determine a variable called an "indicator"
(also referred to as "eHealth Indicator") and its minimum and
maximum interval values. The system then uses this "Indicator"
value to search a look up table for the predefined corresponding
disease, which may be stored on a storage device in communication
with the processor. The storage device may be integral to the
processing system or may be independent of it. The instant system
is experimented on various scenarios and a software simulator has
been developed for evaluation and performance testing.
[0038] As detailed below, the present inventors developed a
systematic procedure for self-diagnosis of diseases, using a
support system developed and tested. In the examples provided, the
system may perform operations that detect potential occurrences of,
and compute indicia of, several medical conditions. Each medical
condition is associated with specific symptoms and signs that are
mapped directly with several kinds of sensors and their readings.
It is to be understood that the types of medical conditions
presented in this disclosure as well as their indicia are only
exemplary and are not intended to limit the scope of the invention.
It is also to be understood that the current system and method may
be used to detect and identify other types of medical conditions,
diseases and indicial thereof
[0039] The instant disease diagnosis approach starts with reading
the user real time vital signs using a wearable sensor system. Any
wearable sensor system known in the art are invention to be used in
this invention. The wearable system may comprise one or more
sensors. It is to be understood that any sensors known in the field
for measuring parameters related to by way of non-limiting example
to physiological, non-physiological, activity, motion and emotional
data may be integrated into the wearable sensor system. In the
current embodiment, two variables are introduced, the "control" to
account for the sensor output range and whether it is normal or not
and the "weighting factor" to determines the significant
contribution of the corresponding sensor. These two parameters and
the actual value of the sensor are used to determine an indicator
value. The system then uses this "indicator" value to search a
predefined disease look up table for the corresponding disease.
This system helps in assessing the physical health of a person by
providing diagnosis of possible diseases and checking treatment
progress toward recovery. Using the instant system and algorithm,
medical condition detection is faster than traditional techniques.
That is, the present inventors observed the performance of
calculating the health Indicator is faster 10% to 48% than the
sequential search method.
A. System Architecture
[0040] In one embodiment, the present inventors developed a system
architecture that permits medical condition detection based on an
Indicator value within minimum or maximum ranges of a defined
medical condition. For instance, and as illustrated in FIG. 1, an
illustrative system architecture may have four stages: (a)
Pre-Defined stage, (b) Pre-Processing Calculations, (c) Processing
operations, and (d) Medical Condition' Detection. [0041] (a)
Pre-Defined stage: in this stage, sensor ranges are setup with
their corresponding minimum and maximum ranges. A weighting factor
is defined as well as the medical conditions. [0042] (b)
Pre-Processing Calculations: in this stage, the sensors values are
captured and stored, and the minimum multiplication for each sensor
is calculated using the weight factor (WF) defined from the
previous stage. [0043] (c) Processing operations: in this stage a
Control value is assigned for each sensor depending on whether its
measured value is normal or abnormal. The control value is binary
and therefor is either 0 or 1. In this stage, the Indicator factor
will also be calculated based on: weight factor assigned to the
sensor, the actual measurement of the sensor and the Control
values. [0044] (d) Medical Condition' Detection: in this stage, the
medical condition is detected based on the Indicator value being
within the minimum or maximum ranges of the defined medical
condition.
[0045] The system may perform operations that detect potential
occurrences of, and compute indicia of several defined medical
conditions. Usually a disease is constructed as a medical condition
associated with specific symptoms and vital signs. Vital signs
normally vary with, for example, age, weight, gender, and overall
health. Measuring the vital signs for a person will provide an
accurate figure about the body's physical status and the health
condition. Due to the technological advancement of the biological
sensor, presently there are dedicated sensors for each vital sign
to capture the corresponding vital sign. Most human diseases are
related to the status of the vital signs and whether their values
are within or beyond the normal ranges. These vital signs are
usually collected using dedicated sensors such as temperature, ECG,
and breathing sensors. It is to be understood that these types of
sensors are only exemplary and are not intended to limit the scope
of the invention. So, it is to be understood that any sensors known
in the art may be used in this invention for the purpose of
measuring vital signs associated with, by way of non-limiting
examples, physiological, non-physiological, activity, motional and
emotional parameters. Also, while this application makes reference
to the patient being a human in some instances, it is to be
understood that such representation is only exemplary. It is also
to be understood that a patient may cover any living organism from
which vital signs may be obtained.
[0046] To accelerate development of a system architecture, the
present inventors used a commercially available platform, namely
e-Health Sensor Platform V2.0. The platform consists of 9 different
wearable sensors which measure 11 vital signs and a shield to
connect the sensors. FIG. 2 illustrates the sensors and the shield.
Of course, it is understood that a similar platform could be used,
and the present disclosure in no way requires a specific platform
or commercial product. Also, while the current platform is shown to
use 9 sensors and measure 11 vital signs, it is to be understood
that this only exemplary and none limiting. In other embodiments,
sensors ranging from 1 to n, where n is a natural number may be
used. Similarly, other embodiment may be used to measure vital
signs corresponding to any combination of all known vital
signs.
[0047] While in no way limiting, Table 2 below provides a brief
description of 9 sensors and the biometrics they measure. The
present system measures 11 different biological signals. Those 11
signals have normal ranges that if a value outside the normal range
has been detected, then the physiological status of the person is
considered abnormal and then used to probably classify the patient
as having a medical condition. The ranges for these signals change
according to many factors such as, for example, age, gender,
location etc. For example, heart rate normal ranges for an infant
if he is awake is between 100 and 190 beats per minute (bpm) but
while he is sleeping the range becomes 90 to 160 bpm. On the other
hand, a sleeping adult normal heart rate is between 50 and 90 bpm
but if he is awake the range becomes 60 to 100 bpm [25].
TABLE-US-00002 TABLE 2 Wearable Health Sensors and the biometric
they measure The Sensor Biometric it measures Pulse and SPO2 sensor
Heart Rate (HR) Arterial oxygen saturation (SPQ2) Airflow sensor
Respiratory rates (RR) Body temperature sensor Body temperature
(TEMP) (ECG) sensor Assess the electrical and muscular functions of
the heart Glucometer Approximate concentration of glucose in the
blood Sphygmomanometer Systolic blood pressure (SBP) Diastolic
blood pressure (DBP) Galvanic skin response Measuring electrical
conductance of the sensor (GSR) skin, which varies with its
moisture level Accelerometer Patient positions
Muscle/electromyography Electrical activity of muscles sensor
(EMG)
[0048] The instant system may store, in one or more tangible,
non-transitory memories, structured data records (e.g., within a
lookup database) that act as reference and facilitate a detection
of a particular medical condition based on biometric and other data
captured by wearable devices in communication with the system
across one or more communications networks. Such communication may
be wired or wireless.
B. Medical Condition Detection
[0049] FIG. 3 describes a medical condition detection system
according to an exemplary embodiment, which includes detecting a
medical condition, or a disease from a list of defined medical
conditions (diseases) based on the calculation of a variable called
an Indicator. First, a disease must be identified. Second, the
symptoms of the disease are specified. Third, the involved sensors
sub ranges are defined. Forth, the maximum and the minimum value
for the involved sensor are established and the corresponding
control value for the involved sensors will be set to `1`. A
weighting factor (WF) value is introduced. The weighting factor is
a unique value assigned to each sensor. This value determines the
significant contribution of the corresponding sensor. The WF value
may vary from " 0" to " 1" . The weighting factor value corresponds
to the frequency of use of a specific kind of sensor in several
medical conditions in the look up table. In other words, for
example, if there are 100 defined medical conditions based on 10
kind of sensors readings and the temperature is included in all of
them, then its corresponding WF is 1, and if it is included in 85
conditions, its WF is 0.85 and so on and so forth. This factor will
be used later in the computation of the "Indicator" value used to
identify the corresponding medical condition. Since the WF depends
on the total number of defined diseases in our database, every time
we add a new disease we update the WF for the involved sensors. It
is contemplated that with addition of more diseases to the look up
table, the accuracy of to system will be enhanced. Fifth, the
maximum and minimum "Indicator" value for the disease is computed
and attached to the corresponding medical condition in the disease
lookup table.
[0050] For instance and by way of non-limiting example, Table 3
below shows the weighting factors for some of the sensors used
according to the defined medical conditions, consistent with the
disclosed exemplary embodiments. In some aspects, certain of the
disclosed systems may store one or more weighting factors in a
corresponding database. The weighting factors numbers assigned to
different type of sensors are listed in Table 3.
TABLE-US-00003 TABLE 3 Sample of the used sensors with their
corresponding weighting factor WFS Sensor Type sensor Abbreviation
0.7 Heart Rate Sensor HR_SENSOR 0.9 Blood Pressure Sensor BP_SENSOR
0.2 Spo2 Sensor SPO2_SENSOR 0.6 Temperature SENSOR TEMP_SENSOR 0.5
Respiration Rate Sensor RR_SENSOR 0.2 Glucose Level SENSOR
GLOCOSE_SENSOR
[0051] It is noted that each sensor has a sensing range. This
sensing range could be divided into small ranges. As an example,
Table 4 below presents the sub ranges for human temperature
sensor's reading as a non-limiting example. In this example, the
sensor has four (4) intervals each with its corresponding range
values. When body temperature falls below 35.0.degree. C., the
subject has hypothermia. Hypothermia is a medical emergency that
occurs when human's body loses heat faster than it can produce
heat, causing a dangerously low body temperature. The normal range
of internal human body temperature varies between
(36.5-37.5).degree. C.
TABLE-US-00004 TABLE 4 Defined human temperature classification
ranges [27][28][29][30][31] Ranges Symptom Interval STR1
Hypothermia <35.0.degree. C. (95.0.degree. F.) STNR Normal
36.5-37.5.degree. C. (97.7-99.5.degree. F.) STR2 Fever >37.5 or
38.3.degree. C. (99.5 or 100.9.degree. F.) STR4 Hyperpyrexia
>40.0 or 41.5.degree. C. (104.0 or 106.7.degree. F.)
"Indicator" Computational Algorithm
[0052] As stated previously, the proposed system starts whenever
subject sensors measurements are available. For each sensor, three
parameters were defined, namely their WF, minimum and maximum
values. The proposed system then uses these values to compute the
corresponding minimum and maximum range values of the "Indicator"
parameter. Table 4 is updated by adding to it a new column that
represents the actual measured value. If the actual measured value
lies in the normal range, the corresponding control value is set to
"0", otherwise, it is set to "1". Based on this, if all the sensors
readings are within their normal ranges, then the `Indicator` value
will be "0", thus no medical condition is detected (diseases free
case). Table 5 shows the indicator computation matrix.
TABLE-US-00005 TABLE 5 Indicator computation matrix Sensor rule WF
Min Max Actual Control S1R WF1 Mini Maxi A1 C1 = "0" or "1" S2R WF2
Min2 Max2 A2 C2 = "0" or "1" S3R WF3 Min3 Max3 A3 C3 = "0" or "1"
S4R WF4 Min4 Max4 A4 C4 = "0" or "1" S5R WF5 Min5 Max5 A5 C5 = "0"
or "1" S6R WF6 Min6 Max6 A6 C6 = "0" or "1" S7R WF7 Min7 Max7 A7 C7
= "0" or "1"
[0053] The developed algorithm is used to determine the "Indicator"
and its minimum and maximum interval values. The system then uses
this Indicator value to search a look up table for the
corresponding disease. The Indicator for a specific disease is
computed using the below formula:
Indicator=.SIGMA..sub.i=1.sup.n (WF.sub.i)(A.sub.i)(C.sub.i)
(1)
and the corresponding minimum (Min_Ind) and maximum (Max_Ind) for
the indicator values for a specific disease are computed using the
following equations:
Min_Ind=.SIGMA..sub.i=1.sup.n (WF.sub.i)(Min.sub.i)(C.sub.i)
(2)
Max_Ind =.SIGMA..sub.i=1.sup.n (WF.sub.i)(Max.sub.i)(C.sub.i)
(3)
where WF.sub.i, A.sub.i, C.sub.i, Min.sub.i, Max.sub.i, and i are
the weighting factor, actual reading of the sensor, control,
minimum, maximum range values, and the number of the sensor,
respectively and where n is a natural number.
[0054] The Min_Ind and the Max_Ind values are computed and saved in
a disease lookup table. Each disease has an interval to identify it
and this interval is defined by the Min_Ind and the Max_Ind values.
Every time a new disease is added to a database, its `Indicator`
interval is defined using equations 2 and 3. The disease lookup
table is implemented as a binary search tree (BST). The BST
facilitate and accelerate the range search process.
[0055] In some embodiments, the look up table may be populated with
entries related to emotional conditions, diseases or abnormalities.
In such embodiments, the system and method of the current
disclosure may be used to identify and detect emotional states,
conditions, diseases disorders and/or abnormalities based on the
measured sensor data and the developed algorithm. Some examples of
the above may include but is not limited to sadness, happiness,
anger, excitement, mania, depression and other emotional conditions
known in the art.
Exemplary Computer-Implemented Processes for Automatic Disease
Detection
[0056] The detailed disease diagnosis overview is shown in FIG. 4.
First, the uservital signs readings are provided to the system. The
sensors whose readings are in the normal range, their index
(control) value will be set to zero and the other sensors control
value will be set to 1. Then, the `Indicator` value is computed
from the actual sensor reading value, the sensor control value and
the sensor weight factor value. If the computed `Indicator` value
equals zero then the user's vital signs are in the normal range but
if the `Indicator` value is greater than zero, this means that the
user is suffering from a specific disease. The `Indicator` value is
then used by the processor to search the disease lookup table for
the corresponding disease and to present it as a suggested
diagnosis.
[0057] In no way limiting and as an example, Table 6 below shows
the structure of the disease lookup table for four medical
conditions. As revealed through equations (2) and (3), the
calculation of the corresponding disease's minimum and maximum
"Indicator" values is independent of the actual real time sensor
reading. Indeed, all parameters used for determining Min_Ind and
Max_Ind are predefined values.
TABLE-US-00006 TABLE 6 Disease lookup table for diagnosis and
identification. Disease Min_Ind Max_Ind MCI Min_Ind1 Max_Ind1 MC2
Min_Ind2 Max_Ind2 MC3 Min_Ind3 Max_Ind3 MC4 Min_Ind4 Max_Ind4
[0058] The instant system does not require any medical information
to be provided and entered by the user manually. Rather, all what
is needed is to connect the sensors to the subject's body. This may
require a one-time training for the user to teach him/her where and
how to place the sensors. In some embodiments where sensors may be
placed in wearables such as watches, fit bits, health bracelets or
the like, the subject's initial training for placement of the
sensors may not be required. The instant system, and certain
exemplary computer-implemented processes described above, may be
implement in addition to, or as an alternate to, known web-based
medical diagnostic tools where the user needs to type his symptoms
manually. In such traditional systems described in the prior art,
it is required that the patient knows the medical terms for the
symptoms he or she is experiencing and their correct spelling.
Also, in such traditional known system and diagnostic tools,
identification of a symptomless diseases, such as Hypertension,
would not be possible. The current invention is advantageous over
such traditional known online diagnostic systems and tools because
it is able to overcome both of these deficiencies. By allowing the
system to rely only on data obtained from the sensors, the system
is able to work passively and eliminate subjectivity of the patient
or physician. Also, by using sensors that are able to collect data
continuously, such as wearables, the system may be considered a
continuous monitoring system.
[0059] The process of detecting diseases using the new algorithm is
depicted as a pseudocode and is exemplified in FIG. 5.
EXAMPLES
System Testing and Evaluation
[0060] In order to demonstrate the applicability of the instant
system and algorithm in real life situations, the inventors
developed the main functions and components and performed various
experimental tests. After that, the inventors conducted several
measurements to evaluate the system' s performance.
[0061] It is understood that the below Examples are illustrative
and non-limiting. It will, however, be evident that various
modifications and changes may be made thereto, and additional
embodiments may be implemented, without departing from the broader
scope of the disclosed embodiments.
Example 1
System Testing Setup
[0062] To validate the instant eHealth architecture and disease
detection algorithm, the inventors developed a test bench, as shown
in FIG. 6. The test bench has three elements: wearable Bluetooth
sensors simulator, the medical gateway, and the eHealth remote
server.
[0063] The simulator enables the simulation of various medical
sensors output. This simulator may be installed on a tablet. In the
actual system, the simulator may be replaced by a set of wearable
medical sensors mounted on the patient (as depicted in FIG. 2).
Digital values of vital signs are sent from the simulator to the
medical gateway using Bluetooth low energy wireless network
technology. Other means of wireless or wired communication may be
used. The medical gateway (an application running on a smart tablet
or other processor or smart device) collects vital signs and
displays them in real time on a display; at the same time these
values are transferred to an eHealth server for further analysis
and disease detection. The eHealth server analyzes vital signs
values using the instant algorithm for disease detection as
explained above. Once a disease or some symptoms have been
detected, the server sends a notification to the patient, (this
notification will be displayed in real time on the display of the
tablet or other device) and an email alert will be sent to the
doctor. In some embodiments, other forms of notifications may also
be triggered. For example, notification may be provided to the
patient in the form of audio or visual notification. It may also be
sent to the patient's email or by form of text message to his
mobile. Notification may also be sent to the hospital or an
emergency contact of the subject or an emergency mobile unit
depending on the severity of the condition or disease identified
and based on pre-set instructions for such notification by the
user.
[0064] In this specific example, a software simulator with a set of
virtual wearable sensors was designed to setup a specific medical
condition. The simulator set of virtual sensors' output is
adjustable and can be manipulated to correspond to a specific
disease.
[0065] FIG. 7 provides an exemplary designed simulator. This hybrid
simulator sensors configuration framework is developed to simulate
continuous dynamics of the human's physiology. The medical
conditions can be simulated by adjusting the slider to a certain
value. A decision was made during the conducted experiments to only
use the first seven sensors. The remaining sensors may be activated
whenever there is a need. The listed medical conditions in Table 1
may be simulated by configuring the first seven sensors only and
the simulator can be updated to include further type of
sensors.
[0066] Different communication protocols are used to transmit the
collected data to the storage and processing servers, i.e.
Bluetooth Smart Ready and WiFi. The Bluetooth protocol was used
because of its short-range connectivity, low power consumption,
high connectivity bandwidth and its lightweight
receiver/transmitter load. While the WiFi protocol was used to
connect the gateway with the cloud servers via the internet due to
its liability, and wide-range (approx. 50 m) connectivity, the
cloud environment was chosen due to its availability, huge
processing capabilities as well as its large storage resources. The
test bed for the experimental setup is depicted in FIG. 8. The
purpose of the experiment is to evaluate the performance of the
instant algorithm in detecting the medical conditions. Those tests
should demonstrate the efficiency of the instant algorithm in
comparison with conventional and linear algorithms. The experiments
should also evaluate system performance in terms of the data
transfer rate and computational time.
Example 2
Bluetooth Data Transfer Time
[0067] FIG. 9 displays the data transfer from the sensors simulator
(Peripheral Device) to the medical gateway (called Central). The
peripheral has an advertisement interval of 300 milliseconds (ms),
however the advertisement time was fixed by the software to 100 ms.
The Central has a scan window of 50 ms and a scan interval of 100
ms. Of course, it is understood that this is a non-limiting
example.
Example 3
[0068] Data Transfer from Gateway to Server
[0069] The second test will evaluate the data transfer time needed
for sending data from the medical gateway to a server. The result
are depicted in FIG. 10 and it shows an average of 155
milliseconds. The x axis represents number of tests run and the y
axis represent the time in milliseconds.
Example 4
Testing Disease Detection Algorithm
[0070] The last test was mainly designed to evaluate the
performance and efficiency of the instant algorithm to detect
disease. To measure the time required for disease detection a
custom script was created, similar to the one executed on eHealth
server to measure the differences between the proposed algorithm
and any conventional algorithm using searching in a normal lookup
table sequential as shown in the pseudocode below in FIG. 11.
[0071] The script includes measurement functions that measures the
times required to execute the following tasks: [0072] eHealth
Indicator calculation time. Disease search time in the lookup
table. [0073] Disease total detection time using eHealth Indicator
and lookup table. [0074] Disease detection time using the
conventional sequential algorithm (vital signs are compared with
the normal and abnormal ranges of each sensor)
TABLE-US-00007 [0074] TABLE 7 Summary of the computation time
lapsed, obtain from the different tests (time in seconds) Disease
Disease eHealth Time to Detection detection Delta Indicator search
time time using time % eHealth Calc. disease using sequential
.DELTA. = (D * TEST Indicator time in lookup Indicator test 100/C)
- N.sup.o value (A) (B) C = A + B (D) 100 Detected diseases 1
376.900 0.000898 0.00015800 0.001056 0.001527 44.63% Severe
Hypertension 2 48.500 0.000786 0.00011500 0.000901 0.001184 31.42%
Hypotension/Diabetes/ Moderate Hypertension 3 176.400 0.000786
0.00011500 0.000901 0.001184 31.42% Asthma Severe/ Moderate
Hypertension 4 189.200 0.000816 0.00021900 0.001035 0.001165 12.53%
Asthma Severe/ Moderate Hypertension 5 0.000 0.001005 0.00015300
0.001158 0.001534 32.43% No disease detection 6 111.400 0.000473
0.00008300 0.000556 0.000795 43.04% Tachycardia/Asthma
Severe/Moderate Hypertension 7 132.300 0.000804 0.00014800 0.000952
0.001305 37.10% Tachycardia/Asthma Severe/Moderate Hypertension 8
21.700 0.000816 0.00021900 0.001035 0.001165 12.53% Bradycardia/
Hypotension/ Respiratory Arrest Imminent/Moderate Hypertension 9
35.000 0.000688 0.00014500 0.000833 0.001235 48.26% Bradycardia/
Hypotension/Pre diabetes/Respiratory Arrest Imminent/ Moderate
Hypertension 10 111.300 0.000898 0.00018400 0.001082 0.001252
15.69% Tachycardia/Asthma Severe/Moderate Hypertension 11 133.000
0.002494 0.00012700 0.002621 0.002945 12.37% Tachycardia/Asthma
Severe/Moderate Hypertension 12 221.000 0.000461 0.00007900
0.000540 0.000632 17.09% Moderate Hypertension 13 53.800 0.000868
0.00024200 0.001110 0.001351 21.67% Hypotension/Diabetes/ Moderate
Hypertension 14 64.200 0.000919 0.00015000 0.001069 0.001561 46.00%
Hypotension/Diabetes/ Moderate Hypertension 15 94.800 0.000847
0.00015600 0.001003 0.001403 39.93% Tachycardia/Asthma
Moderate/Moderate Hypertension 16 71.400 0.000873 0.00019300
0.001066 0.001245 16.80% Tachycardia/ Hypotension/Diabetes/
Moderate Hypertension 17 30.500 0.000889 0.00019500 0.001084
0.001554 43.32% Bradycardia/ Hypotension/Pre diabetes/Respiratory
Arrest Imminent/ Moderate Hypertension 18 376.900 0.000994
0.00019600 0.001190 0.001434 20.49% Severe Hypertension 19 48.500
0.000899 0.00016300 0.001062 0.001190 12.02% Hypotension/Diabetes/
Moderate Hypertension 20 21.700 0.000559 0.00010300 0.000662
0.000733 10.66% Bradycardia/ Hypotension/ Respiratory Arrest
Imminent/Moderate Hypertension 21 166.500 0.000873 0.00019300
0.001066 0.001245 16.80% Asthma Severe/ Moderate Hypertension 22
8.800 0.005223 0.00020700 0.005430 0.006163 13.49% Bradycardia/
Hypotension/ Hypoxaemia/Tachypnea/ Moderate Hypertension 23 195.300
0.000780 0.00013800 0.000918 0.001296 41.15% Asthma Severe/
Moderate Hypertension 24 241.300 0.001247 0.00021000 0.001457
0.001674 14.89% Moderate Hypertension 25 221.000 0.000461
0.00007900 0.000540 0.000775 43.43% Moderate Hypertension 26
221.000 0.000878 0.00015100 0.001029 0.001284 24.74% Moderate
Hypertension 27 264.600 0.000878 0.00015100 0.001029 0.001284
24.74% Severe Hypertension
[0075] The comparison chart shown below (FIG. 12) shows the disease
detection time using the eHealth Indicator and the lookup table. It
is clear that the instant algorithm is much faster than the
conventional algorithm using the sequential test. The instant
algorithm uses an access to the database in order to get real-time
vital signs and to check the medical conditions. The calculation
time change depending on the server load. Therefore, the tests were
conducted on a dedicated local host instead of cloud-based server
to avoid the server load factor. During all the tests conducted, it
was observed that the performance of the method and system used in
the instant algorithm for calculating the health Indicator is
faster 10.66% to 48.26% than the sequential search method.
[0076] Compared to the conventional linear search (sequential
search) method for finding the target rule in a list and trigger
its action, the sequential search method checks each and every rule
in the list until it finds the matching rule or all the rules are
searched without finding a match. An online tool has been developed
to test the instant algorithm's performance on real-time in
detecting the diseases and improve the performance as fast as
possible. FIG. 13 provides a screenshot of the online test.
[0077] For example, to detect a " Severe Hypertension" by both
algorithms based on the given vital signs by the sensors, the
sequential " Serial" search algorithm elapsed 173 milliseconds to
detect the disease, while the Indicator algorithm lapsed only 129
milliseconds to detect the same. This raises the performance of the
diagnostic system and method to up to 34% for this particular
medical condition. Further examples of diagnostic indicators are
shown in Table 8.
TABLE-US-00008 TABLE 8 Further examples of diagnostic indicators
Disease Description Vital signs ranges Associated sensor/s
Bradycardia abnormally slow heart rate <60 beats/min HR_SENSOR
Tachycardia abnormally fast heart rate >100 OR > 120
beats/min HR_SENSOR Hypotension abnormally low blood pressure BP
< 100 mm Hg systolic BP_SENSOR Hypertension abnormally high
blood pressure Mild to moderate (systolic BP_SENSOR blood pressure
< 180 mm Hg and diastolic blood pressure below 110 mm Hg) Severe
hypertension, BP_SENSOR defined as a systolic pressure > 180 mm
Hg or diastolic pressure > 110 mm Hg, Hypoxaemia abnormally low
concentration of oxygen SP02 < 95% SP02_SENSOR in the blood
Hyperthermia abnormally high body temperature core temperature >
37.80.degree. C. TEMP_SENSOR Hypothermia Abnormally low body
temperature core temperature < 36.0.degree. C. TEMP_SENSOR
Bradypnea abnormally slow breathing rate RR < 20 breaths/min
RR_SENSOR Tachypnea abnormally fast breathing rate RR > 25
breaths/min RR_SENSOR Sinus P waves are hidden within each
preceding ECG image "camel hump" ECG_SENSOR Tachycardia T wave
appearance Prediabetes blood sugar level is higher than normal
Fasting glucose level: GLOCOSE_SENSOR but not yet high enough to be
classified as (100-125) (mg/dL) type 2 diabetes Diabetes describes
a group of metabolic diseases in Fasting glucose level:
GLOCOSE_SENSOR which the person has high blood glucose more than
125 (mg/dL) (blood sugar), either because insulin production is
inadequate, or because the body's cells do not respond properly to
insulin, or both Pneumonia a disease of the lungs characterized RR
> 25 breaths/min RR_SENSOR especially by inflammation and HR
> 100 OR HR > 120 beats/min HR_SENSOR consolidation of lung
tissue followed by core temperature > 37.80.degree. C.
TEMP_SENSOR resolution and by fever, chills, cough, and difficulty
in breathing and that is caused especially by infection Urosepsis
is a systemic reaction of the body (SIRS) core temperature >
37.80.degree. C. TEMP_SENSOR to a bacterial infection of the
urogenital HR > 100 or HR > 120 beats/min HR_SENSOR organs
with the risk of life-threatening BP < 100 mm Hg systolic
BP_SENSOR symptoms including shock Asthma is a chronic inflammatory
disorder of the 90% < SPO2 < 95% SP02_SENSOR Moderate airways
100 < HR < 120 beats/min HR_SENSOR RR > 25 breaths/min
RR_SENSOR Asthma is sever chronic inflammatory disorder of SP02
< 90% SP02_SENSOR Severe the airways HR > 120 beats/min
HR_SENSOR RR > 25 breaths/min RR_SENSOR Respiratory is the
cessation of normal breathing due SP02 < 90% SP02_SENSOR Arrest
to failure of the lungs to function effectively HR < 60
beats/min HR_SENSOR Imminent RR > 30 breaths/min RR_SENSOR
Exemplary Hardware and Software Implementations
[0078] Embodiments of the subject matter and the functional
operations described in this specification can be implemented in
digital electronic circuitry, in tangibly-embodied computer
software or firmware, in computer hardware, including the
structures disclosed in this specification and their structural
equivalents, or in combinations of one or more of them. Embodiments
of the subject matter described in this specification, can be
implemented as one or more computer programs, i.e., one or more
modules of computer program instructions encoded on a tangible non
transitory program carrier for execution by, or to control the
operation of, data processing apparatus. Additionally or
alternatively, the program instructions can be encoded on an
artificially generated propagated signal, such as a
machine-generated electrical, optical, or electromagnetic signal
that is generated to encode information for transmission to
suitable receiver apparatus for execution by a data processing
apparatus. The computer storage medium can be a machine-readable
storage device, a machine-readable storage substrate, a random or
serial access memory device, or a combination of one or more of
them.
[0079] The term "data processing apparatus" refers to data
processing hardware and encompasses all kinds of apparatus,
devices, and machines for processing data, including by way of
example a programmable processor, a computer, or multiple
processors or computers. The apparatus can also be or further
include special purpose logic circuitry, such as an FPGA (field
programmable gate array) or an ASIC (application specific
integrated circuit). The apparatus can optionally include, in
addition to hardware, code that creates an execution environment
for computer programs, such as code that constitutes processor
firmware, a protocol stack, a database management system, an
operating system, or a combination of one or more of them.
[0080] A computer program, which may also be referred to or
described as a program, software, a software application, a module,
a software module, a script, or code, can be written in any form of
programming language, including compiled or interpreted languages,
or declarative or procedural languages, and it can be deployed in
any form, including as a stand alone program or as a module,
component, subroutine, or other unit suitable for use in a
computing environment. A computer program may, but need not,
correspond to a file in a file system. A program can be stored in a
portion of a file that holds other programs or data, such as one or
more scripts stored in a markup language document, in a single file
dedicated to the program in question, or in multiple coordinated
files, such as files that store one or more modules, sub programs,
or portions of code. A computer program can be deployed to be
executed on one computer or on multiple computers that are located
at one site or distributed across multiple sites and interconnected
by a communication network.
[0081] The processes and logic flows described in this
specification can be performed by one or more programmable
computers executing one or more computer programs to perform
functions by operating on input data and generating output. The
processes and logic flows can also be performed by, and apparatus
can also be implemented as, special purpose logic circuitry, such
as an FPGA (field programmable gate array) or an ASIC (application
specific integrated circuit).
[0082] Computers suitable for the execution of a computer program
include, by way of example, general or special purpose
microprocessors or both, or any other kind of central processing
unit. Generally, a central processing unit will receive
instructions and data from a read only memory or a random access
memory or both. The essential elements of a computer are a central
processing unit for performing or executing instructions and one or
more memory devices for storing instructions and data. Generally, a
computer will also include, or be operatively coupled to receive
data from or transfer data to, or both, one or more mass storage
devices for storing data, such as magnetic, magneto optical disks,
or optical disks. However, a computer need not have such devices.
Moreover, a computer can be embedded in another device, such as a
mobile telephone, a personal digital assistant (PDA), a mobile
audio or video player, a game console, a Global Positioning System
(GPS) receiver, or a portable storage device, such as a universal
serial bus (USB) flash drive, to name just a few.
[0083] Computer readable media suitable for storing computer
program instructions and data include all forms of non-volatile
memory, media and memory devices, including by way of example
semiconductor memory devices, such as EPROM, EEPROM, and flash
memory devices; magnetic disks, such as internal hard disks or
removable disks; magneto optical disks; and CD ROM and DVD-ROM
disks. The processor and the memory can be supplemented by, or
incorporated in, special purpose logic circuitry.
[0084] To provide for interaction with a user, embodiments of the
subject matter described in this specification can be implemented
on a computer having a display device, such as a CRT (cathode ray
tube) or LCD (liquid crystal display) monitor, for displaying
information to the user and a keyboard and a pointing device, such
as a mouse or a trackball, by which the user can provide input to
the computer. Other kinds of devices can be used to provide for
interaction with a user as well; for example, feedback provided to
the user can be any form of sensory feedback, such as visual
feedback, auditory feedback, or tactile feedback; and input from
the user can be received in any form, including acoustic, speech,
or tactile input. In addition, a computer can interact with a user
by sending documents to and receiving documents from a device that
is used by the user; for example, by sending web pages to a web
browser on a user' s device in response to requests received from
the web browser.
[0085] Implementations of the subject matter described in this
specification can be implemented in a computing system that
includes a back end component, such as a data server, or that
includes a middleware component, such as an application server, or
that includes a front end component, such as a client computer
having a graphical user interface or a Web browser through which a
user can interact with an implementation of the subject matter
described in this specification, or any combination of one or more
such back end, middleware, or front end components. The components
of the system can be interconnected by any form or medium of
digital data communication, such as a communication network.
Examples of communication networks include a local area network
(LAN) and a wide area network (WAN), such as the Internet.
[0086] The computing system can include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other. In some implementations,
a server transmits data, such as an HTML page, to a user device,
such as for purposes of displaying data to and receiving user input
from a user interacting with the user device, which acts as a
client. Data generated at the user device, such as a result of the
user interaction, can be received from the user device at the
server.
[0087] While this specification contains many specifics, these
should not be construed as limitations, but rather as descriptions
of features specific to particular embodiments. Certain features
that are described in this specification in the context of separate
embodiments may also be implemented in combination in a single
embodiment. Conversely, various features that are described in the
context of a single embodiment may also be implemented in multiple
embodiments separately or in any suitable sub-combination.
Moreover, although features may be described above as acting in
certain combinations and even initially claimed as such, one or
more features from a claimed combination may in some cases be
excised from the combination, and the claimed combination may be
directed to a sub-combination or variation of a
sub-combination.
[0088] Similarly, while operations are depicted in the drawings in
a particular order, this should not be understood as requiring that
such operations be performed in the particular order shown or in
sequential order, or that all illustrated operations be performed,
to achieve desirable results. In certain circumstances,
multitasking and parallel processing may be advantageous. Moreover,
the separation of various system components in the embodiments
described above should not be understood as requiring such
separation in all embodiments, and it should be understood that the
described program components and systems may generally be
integrated together in a single software product or packaged into
multiple software products.
[0089] In each instance where an HTML file is mentioned, other file
types or formats may be substituted. For instance, an HTML file may
be replaced by an XML, JSON, plain text, or other types of files.
Moreover, where a table or hash table is mentioned, other data
structures (such as spreadsheets, relational databases, or
structured files) may be used.
[0090] While this specification contains many specifics, these
should not be construed as limitations, but rather as descriptions
of features specific to particular implementations. Certain
features that are described in this specification in the context of
separate implementations may also be implemented in combination in
a single implementation. Conversely, various features that are
described in the context of a single implementation may also be
implemented in multiple implementations separately or in any
suitable sub-combination. Moreover, although features may be
described above as acting in certain combinations and even
initially claimed as such, one or more features from a claimed
combination may in some cases be excised from the combination, and
the claimed combination may be directed to a sub-combination or
variation of a sub-combination.
[0091] Similarly, while operations are depicted in the drawings in
a particular order, this should not be understood as requiring that
such operations be performed in the particular order shown or in
sequential order, or that all illustrated operations be performed,
to achieve desirable results. In certain circumstances,
multitasking and parallel processing maybe advantageous. Moreover,
the separation of various system components in the implementations
described above should not be understood as requiring such
separation in all implementations, and it should be understood that
the described program components and systems may generally be
integrated together in a single software product or packaged into
multiple software products.
[0092] Various embodiments have been described herein with
reference to the accompanying drawings. It will, however, be
evident that various modifications and changes may be made thereto,
and additional embodiments may be implemented, without departing
from the broader scope of the disclosed embodiments.
[0093] Further, other embodiments will be apparent to those skilled
in the art from consideration of the specification and practice of
one or more embodiments of the present disclosure. It is intended,
therefore, that this disclosure and the examples herein be
considered as exemplary only.
Interpretation of Terms
[0094] Unless the context clearly requires otherwise, throughout
the description and the claims: [0095] "comprise," "comprising,"
and the like are to be construed in an inclusive sense, as opposed
to an exclusive or exhaustive sense; that is to say, in the sense
of "including, but not limited to". [0096] "connected," "coupled,"
or any variant thereof, means any connection or coupling, either
direct or indirect, between two or more elements; the coupling or
connection between the elements can be physical, logical, or a
combination thereof. [0097] "patient", "subject" or "user" or any
variations thereof refers to any recipient of healthcare services.
[0098] "physiological data" refers to data associated with
physiological parameters of the patient. The physiological
parameters include, but not limited to, body temperature, hearth
rate, body exhilaration and respiration rate. [0099] "herein,"
"above," "below," and words of similar import, when used to
describe this specification shall refer to this specification as a
whole and not to any particular portions of this specification.
[0100] "or," in reference to a list of two or more items, covers
all of the following interpretations of the word: any of the items
in the list, all of the items in the list, and any combination of
the items in the list. [0101] the singular forms "a", "an" and
"the" also include the meaning of any appropriate plural forms.
[0102] Words that indicate directions such as "vertical",
"transverse", "horizontal", "upward", "downward", "forward",
"backward", "inward", "outward", "vertical", "transverse", "left",
"right", "front", "back", "top", "bottom", "below", "above",
"under", "upper", "lower" and the like, used in this description
and any accompanying claims (where present) depend on the specific
orientation of the apparatus described and illustrated. The subject
matter described herein may assume various alternative
orientations. Accordingly, these directional terms are not strictly
defined and should not be interpreted narrowly.
[0103] Where a component (e.g. a circuit, module, assembly, device,
etc.) is referred to above, unless otherwise indicated, reference
to that component (including a reference to a "means") should be
interpreted as including as equivalents of that component any
component which performs the function of the described component
(i.e., that is functionally equivalent), including components which
are not structurally equivalent to the disclosed structure which
performs the function in the illustrated exemplary embodiments of
the invention.
[0104] Specific examples of device and method have been described
herein for purposes of illustration. These are only examples. The
technology provided herein can be applied to system and method
other than the examples described above. Many alterations,
modifications, additions, omissions and permutations are possible
within the practice of this invention. This invention includes
variations on described embodiments that would be apparent to the
skilled addressee, including variations obtained by: replacing
features, elements and/or acts with equivalent features, elements
and/or acts; mixing and matching of features, elements and/or acts
from different embodiments; combining features, elements and/or
acts from embodiments as described herein with features, elements
and/or acts of other technology; and/or omitting combining
features, elements and/or acts from described embodiments.
[0105] It is therefore intended that the following appended claims
and claims hereafter introduced are interpreted to include all such
modifications, permutations, additions, omissions and
sub-combinations as may reasonably be inferred. The scope of the
claims should not be limited by the preferred embodiments set forth
in the examples, but should be given the broadest interpretation
consistent with the description as a whole.
* * * * *